Indicator Heterogeneity I: Temporal Heterogeneity
Addison
PARCOMPUTE = TRUE
N_CORE = parallel::detectCores()Background
In this notebook, we repeat the analysis of 02_temporal_heterogeneity.Rmd for all of our core indicators.
Data setup
# Fetch the following sources and signals from the API
# TODO: Add Google Symptoms "eventually"
source_names = c("doctor-visits", "fb-survey", "fb-survey", "hospital-admissions")
signal_names = c("smoothed_adj_cli", "smoothed_cli", "smoothed_hh_cmnty_cli",
"smoothed_adj_covid19")
pretty_names = c("Doctor visits", "Facebook CLI", "Facebook CLI-in-community",
"Hospitalizations")
target_names = c("Cases", "Cases", "Cases", "Deaths")
geo_level = "county"
start_day = "2020-04-15"
end_day = NULL
cache_fname = 'cached_data/03_heterogeneity_core_indicators.RDS'
if (!file.exists(cache_fname)) {
df_signals = vector("list", length(signal_names))
for (i in 1:length(signal_names)) {
df_signals[[i]] = suppressWarnings(
covidcast_signal(source_names[i], signal_names[i],
start_day, end_day,
geo_type=geo_level))
}
# Fetch USAFacts confirmed case incidence proportion (smoothed with 7-day
# trailing average)
df_cases = suppressWarnings(
covidcast_signal("usa-facts", "confirmed_7dav_incidence_prop",
start_day, end_day,
geo_type=geo_level))
df_deaths = suppressWarnings(
covidcast_signal("usa-facts", "deaths_7dav_incidence_prop",
start_day, end_day,
geo_type=geo_level))
saveRDS(list(df_signals, df_cases, df_deaths), cache_fname)
} else {
cached_data = readRDS(cache_fname)
df_signals = cached_data[[1]]
df_cases = cached_data[[2]]
df_deaths = cached_data[[3]]
}
case_num = 500
geo_values = suppressWarnings(covidcast_signal("usa-facts", "confirmed_cumulative_num",
max(df_cases$time_value),
max(df_cases$time_value))) %>%
filter(value >= case_num) %>% pull(geo_value)## Fetched day 2020-11-09: 1, success, num_entries = 3192geo_values = suppressWarnings(covidcast_signal("usa-facts", "confirmed_cumulative_num",
'2020-11-01',
'2020-11-01')) %>%
filter(value >= case_num) %>% pull(geo_value)## Fetched day 2020-11-01: 1, success, num_entries = 3192Setup
sensorize_time_ranges = list(
c(-14, -8),
c(-21, -8),
c(-35, -8),
c(-42, -8))
for (ind_idx in 1:length(source_names)) {
if (target_names[ind_idx] == 'Cases') {
df_target = df_cases
} else if (target_names[ind_idx] == 'Deaths') {
df_target = df_deaths
} else {
stop(sprintf("No matching dataframe for target %s.", target_names[ind_idx]))
}
ind_df = tibble(df_signals[[ind_idx]]) %>% filter(geo_value %in% geo_values)
ind_target = inner_join(ind_df, tibble(df_target),
by=c('geo_value', 'time_value')) %>% select (
geo_value=geo_value,
time_value=time_value,
indicator_value=value.x,
target_value=value.y,
)
ind_global_sensorized = ind_target %>% group_by (
geo_value,
) %>% group_modify ( ~ {
fit = lm(target_value ~ indicator_value, data =.x);
tibble(time_value=.x$time_value,
indicator_value=.x$indicator_value,
target_value=.x$target_value,
sensorized_value=fit$fitted.values)
}) %>% ungroup
df_global_sensorized = ind_global_sensorized %>% transmute (
geo_value=geo_value,
signal='ind_sensorized',
time_value=time_value,
direction=NA,
issue=lubridate::ymd('2020-11-01'),
lag=NA,
value=sensorized_value,
stderr=NA,
sample_size=NA,
data_source='linear_sensorization',
)
attributes(df_global_sensorized)$geo_type = 'county'
attributes(df_global_sensorized)$metadata$geo_type = 'county'
class(df_global_sensorized) = c("covidcast_signal", "data.frame")
base_cor_fname = sprintf('results/08_base_cors_%s_%s.RDS',
source_names[ind_idx], signal_names[ind_idx])
if (!file.exists(base_cor_fname)) {
df_cor_base_ind = covidcast_cor(df_signals[[ind_idx]], df_target,
by='time_value', method='spearman')
df_cor_sensorized_ind = covidcast_cor(df_global_sensorized, df_target,
by='time_value', method='spearman')
df_cor_base = rbind(df_cor_base_ind, df_cor_sensorized_ind)
df_cor_base$Indicator = as.factor(c(rep('Raw', nrow(df_cor_base_ind)),
rep('Sensorized (Spatial)',
nrow(df_cor_sensorized_ind))))
saveRDS(df_cor_base, base_cor_fname)
} else {
df_cor_base = readRDS(base_cor_fname)
}
sensorize_fname = sprintf('results/08_sensorize_cors_%s_%s.RDS',
source_names[ind_idx], signal_names[ind_idx])
sensorize_val_fname = sprintf('results/08_sensorize_vals_%s_%s.RDS',
source_names[ind_idx], signal_names[ind_idx])
if (!file.exists(sensorize_fname)) {
sensorize_cors = vector('list', length(sensorize_time_ranges))
ind_target_sensorized_list = vector('list', length(sensorize_time_ranges))
for (outer_idx in 1:length(sensorize_time_ranges)) {
sensorize_llim = sensorize_time_ranges[[outer_idx]][1]
sensorize_ulim = sensorize_time_ranges[[outer_idx]][2]
min_sensorize_date = lubridate::ymd(start_day) - sensorize_llim
max_sensorize_date = max(ind_target$time_value)
sensorize_date_offsets = 0:(max_sensorize_date-min_sensorize_date)
joiner_df_list = vector('list', length(sensorize_date_offsets))
for (idx in 1:length(sensorize_date_offsets)) {
dt = sensorize_date_offsets[idx]
sensorize_date = min_sensorize_date + dt
joiner_df_list[[idx]] = tibble(
sensorize_date = sensorize_date,
time_value = sensorize_date + sensorize_llim:sensorize_ulim)
}
joiner_df = bind_rows(joiner_df_list)
if (!PARCOMPUTE) {
ind_sensorized_lm = ind_target %>% full_join(
joiner_df,
on='time_value',
) %>% group_by (
geo_value,
sensorize_date,
) %>% group_modify (
~ broom::tidy(lm(target_value ~ indicator_value, data = .x))
) %>% ungroup
} else {
ind_grouped_list = ind_target %>% full_join(
joiner_df,
on='time_value',
) %>% group_by (
geo_value,
sensorize_date,
) %>% group_split
ind_sensorized_lm = parallel::mclapply(ind_grouped_list, function(df) {
broom::tidy(
lm(target_value ~ indicator_value, data = df)
) %>% mutate (
geo_value = unique(df$geo_value),
sensorize_date = unique(df$sensorize_date),
)}, mc.cores = N_CORE) %>% bind_rows
}
ind_sensorized_wide = ind_sensorized_lm %>% select(
geo_value,
sensorize_date,
term,
estimate,
) %>% mutate (
term = sapply(term, function(x) {ifelse(x=='(Intercept)',
'intercept',
'slope')}),
) %>% pivot_wider (
id_cols = c(geo_value, sensorize_date),
names_from=term,
values_from=estimate,
)
ind_target_sensorized = ind_target %>% inner_join (
ind_sensorized_wide,
by=c('time_value'='sensorize_date',
'geo_value'),
) %>% mutate (
sensorized_value = intercept + indicator_value * slope,
)
df_sensorized = ind_target_sensorized %>% transmute (
geo_value=geo_value,
signal='ind_sensorized',
time_value=time_value,
direction=NA,
issue=lubridate::ymd('2020-11-01'),
lag=NA,
value=sensorized_value,
stderr=NA,
sample_size=NA,
data_source='linear_sensorization',
)
attributes(df_sensorized)$geo_type = 'county'
class(df_sensorized) = c("covidcast_signal", "data.frame")
df_cor_sensorized_ind = covidcast_cor(df_sensorized, df_target,
by='time_value', method='spearman')
df_cor_sensorized_ind$Indicator = sprintf('Sensorized (TS, %d:%d)',
sensorize_llim,
sensorize_ulim)
sensorize_cors[[outer_idx]] = df_cor_sensorized_ind
ind_target_sensorized_list[[outer_idx]] = ind_target_sensorized
}
saveRDS(sensorize_cors, sensorize_fname)
saveRDS(ind_target_sensorized_list, sensorize_val_fname)
} else {
sensorize_cors = readRDS(sensorize_fname)
ind_target_sensorized_list = readRDS(sensorize_val_fname)
}
df_cor = bind_rows(df_cor_base, sensorize_cors)
df_cor$Indicator = factor(df_cor$Indicator,
levels=c('Raw',
'Sensorized (Spatial)',
sapply(sensorize_time_ranges,
function(x) {
sprintf('Sensorized (TS, %d:%d)',
x[[1]], x[[2]])
})))
plt = ggplot(df_cor, aes(x = time_value, y = value)) +
geom_line(aes(color = Indicator)) +
labs(title = sprintf("Correlation between %s and %s",
pretty_names[ind_idx],
target_names[ind_idx]),
subtitle = "Per day",
x = "Date", y = "Correlation") +
theme(legend.position = "bottom")
print(plt)
}## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"## Warning: Removed 124 row(s) containing missing values (geom_path).## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Warning: Removed 112 row(s) containing missing values (geom_path).## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Warning: Removed 112 row(s) containing missing values (geom_path).## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Joining, by = "time_value"
## Warning: Removed 370 row(s) containing missing values (geom_path).
QUANTS = c(0.01, 0.99)
# TODO: Add more "core indicators"
for (ind_idx in 1:length(source_names)) {
if (target_names[ind_idx] == 'Cases') {
df_target = df_cases
} else if (target_names[ind_idx] == 'Deaths') {
df_target = df_deaths
} else {
stop(sprintf("No matching dataframe for target %s.", target_names[ind_idx]))
}
base_cor_fname = sprintf('results/08_base_cors_%s_%s.RDS',
source_names[ind_idx], signal_names[ind_idx])
sensorize_fname = sprintf('results/08_sensorize_cors_%s_%s.RDS',
source_names[ind_idx], signal_names[ind_idx])
sensorize_val_fname = sprintf('results/08_sensorize_vals_%s_%s.RDS',
source_names[ind_idx], signal_names[ind_idx])
df_cor_base = readRDS(base_cor_fname)
sensorize_cors = readRDS(sensorize_fname)
sensorized_vals = readRDS(sensorize_val_fname)
for (inner_idx in 1:length(sensorize_time_ranges)) {
sv = sensorized_vals[[inner_idx]]
print(summary(sv$slope))
print(slope_limits <- quantile(sv$slope, QUANTS, na.rm=TRUE))
plt = ggplot(
sensorized_vals[[inner_idx]],
aes(x=time_value,
y=slope),
) + geom_point (
alpha=0.1,
size=0.5,
) + geom_hline (
yintercept=0,
colour='white',
) + stat_summary (
aes(y=slope,
group=1,
colour='median'),
fun=median,
geom="line",
group=1,
) + stat_summary (
aes(y=slope,
group=1,
colour='+/- mad'),
fun=function(x) { median(x) + mad(x) },
geom="line",
group=1,
) + stat_summary (
aes(y=slope,
group=1,
colour='+/- mad'),
fun=function(x) { median(x) - mad(x) },
geom="line",
group=1,
) + scale_colour_manual(
values=c("median"="maroon",
"+/- mad"="darkgreen")
) + labs(
colour=''
) + ggtitle(
sprintf("Slope distribution for %s, fitted on t in %d:%d",
pretty_names[ind_idx],
sensorize_time_ranges[[inner_idx]][1],
sensorize_time_ranges[[inner_idx]][2])
) + ylim (
slope_limits[[1]], slope_limits[[2]]
)
print(plt)
}
}## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -3488425 -1 0 89 2 29243779 4347
## 1% 99%
## -25.07104 26.73099## Warning: Removed 10097 rows containing non-finite values (stat_summary).
## Warning: Removed 10097 rows containing non-finite values (stat_summary).
## Warning: Removed 10097 rows containing non-finite values (stat_summary).## Warning: Removed 10097 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -3693626 -1 0 212 2 49344442 2405
## 1% 99%
## -15.79764 19.26545## Warning: Removed 8001 rows containing non-finite values (stat_summary).## Warning: Removed 8001 rows containing non-finite values (stat_summary).
## Warning: Removed 8001 rows containing non-finite values (stat_summary).## Warning: Removed 8001 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -630174 0 0 368 2 49344442 975
## 1% 99%
## -9.51338 15.64690## Warning: Removed 6177 rows containing non-finite values (stat_summary).## Warning: Removed 6177 rows containing non-finite values (stat_summary).
## Warning: Removed 6177 rows containing non-finite values (stat_summary).## Warning: Removed 6177 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -207823 0 0 392 2 49344442 501
## 1% 99%
## -7.92156 14.74046## Warning: Removed 5487 rows containing non-finite values (stat_summary).## Warning: Removed 5487 rows containing non-finite values (stat_summary).
## Warning: Removed 5487 rows containing non-finite values (stat_summary).## Warning: Removed 5487 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -155949.92 -2.41 0.04 -1.64 2.81 16453.59 4936
## 1% 99%
## -48.03614 49.71457## Warning: Removed 7592 rows containing non-finite values (stat_summary).## Warning: Removed 7592 rows containing non-finite values (stat_summary).
## Warning: Removed 7592 rows containing non-finite values (stat_summary).## Warning: Removed 7592 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -155949.92 -2.17 0.12 -0.46 2.90 5339.24 1903
## 1% 99%
## -31.80537 38.11107## Warning: Removed 4529 rows containing non-finite values (stat_summary).## Warning: Removed 4529 rows containing non-finite values (stat_summary).
## Warning: Removed 4529 rows containing non-finite values (stat_summary).## Warning: Removed 4529 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -1161.5325 -1.6018 0.3529 1.5501 3.4250 666.9360 647
## 1% 99%
## -22.18221 37.48241## Warning: Removed 3089 rows containing non-finite values (stat_summary).## Warning: Removed 3089 rows containing non-finite values (stat_summary).
## Warning: Removed 3089 rows containing non-finite values (stat_summary).## Warning: Removed 3089 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -689.6671 -1.3812 0.5078 2.1473 3.8167 311.7799 330
## 1% 99%
## -19.66630 38.44375## Warning: Removed 2652 rows containing non-finite values (stat_summary).## Warning: Removed 2652 rows containing non-finite values (stat_summary).
## Warning: Removed 2652 rows containing non-finite values (stat_summary).## Warning: Removed 2652 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -300.3856 -0.2228 0.0514 0.1542 0.4426 235.5967 1310
## 1% 99%
## -3.485709 4.689374## Warning: Removed 4026 rows containing non-finite values (stat_summary).## Warning: Removed 4026 rows containing non-finite values (stat_summary).
## Warning: Removed 4026 rows containing non-finite values (stat_summary).## Warning: Removed 4026 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -39.5490 -0.1547 0.1007 0.2514 0.5428 40.3928 584
## 1% 99%
## -2.733614 4.406964## Warning: Removed 3236 rows containing non-finite values (stat_summary).## Warning: Removed 3236 rows containing non-finite values (stat_summary).
## Warning: Removed 3236 rows containing non-finite values (stat_summary).## Warning: Removed 3236 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -24.57113 -0.04043 0.22221 0.43159 0.74530 63.28586 235
## 1% 99%
## -1.676326 3.996921## Warning: Removed 2677 rows containing non-finite values (stat_summary).## Warning: Removed 2677 rows containing non-finite values (stat_summary).
## Warning: Removed 2677 rows containing non-finite values (stat_summary).## Warning: Removed 2677 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -22.00495 -0.00455 0.29104 0.50421 0.84285 63.28586 115
## 1% 99%
## -1.379528 3.877464## Warning: Removed 2433 rows containing non-finite values (stat_summary).## Warning: Removed 2433 rows containing non-finite values (stat_summary).
## Warning: Removed 2433 rows containing non-finite values (stat_summary).## Warning: Removed 2433 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -57.2853 -0.0336 0.0000 0.0093 0.0415 45.9071 1067
## 1% 99%
## -1.017554 1.080987## Warning: Removed 2731 rows containing non-finite values (stat_summary).## Warning: Removed 2731 rows containing non-finite values (stat_summary).
## Warning: Removed 2731 rows containing non-finite values (stat_summary).## Warning: Removed 2731 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -27.9652 -0.0237 0.0000 0.0059 0.0336 35.3041 616
## 1% 99%
## -0.6052395 0.6045198## Warning: Removed 2244 rows containing non-finite values (stat_summary).## Warning: Removed 2244 rows containing non-finite values (stat_summary).
## Warning: Removed 2244 rows containing non-finite values (stat_summary).## Warning: Removed 2244 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -60.00289 -0.01387 0.00070 0.00216 0.02815 35.30415 244
## 1% 99%
## -0.3349906 0.3303182## Warning: Removed 1720 rows containing non-finite values (stat_summary).## Warning: Removed 1720 rows containing non-finite values (stat_summary).
## Warning: Removed 1720 rows containing non-finite values (stat_summary).## Warning: Removed 1720 rows containing missing values (geom_point).
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -46.76855 -0.01128 0.00293 0.00459 0.02795 35.30415 119
## 1% 99%
## -0.2643551 0.2711798## Warning: Removed 1493 rows containing non-finite values (stat_summary).## Warning: Removed 1493 rows containing non-finite values (stat_summary).
## Warning: Removed 1493 rows containing non-finite values (stat_summary).## Warning: Removed 1493 rows containing missing values (geom_point).